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Sayyareh A, Panahi H. Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples. JIRSS. 2017; 17
URL: http://jirss.irstat.ir/article-1-382-fa.html
سیاره عبدالرضا، پناهی هانیه. Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples. پژوهشنامه انجمن آمار ایران. 1396; 17 ()

URL: http://jirss.irstat.ir/article-1-382-fa.html

دکتری دکتر
چکیده:   (61 مشاهده)

The aim of statistical modeling is to identify the model that most closely approximates the underlying process‎. ‎Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation‎. ‎In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid censoring scheme‎. ‎Asymptotic properties of maximum likelihood estimator based on the missing information principle are derived‎. ‎Also‎, ‎asymptotic distribution of the normalized difference of AIC’s is obtained and it is used to construct an interval‎, ‎say tracking interval‎, ‎for comparing the two competing models‎. ‎Monte Carlo simulations are performed to examine how the proposed interval works for different censoring scheme‎. ‎Two real datasets have been analyzed for illustrative purposes‎. ‎The first is selecting between Weibull and generalized exponential distributions for main component of spearmint essential oil purification data‎. ‎The second is the choice between models of the lifetimes of 20 electronic components.

نوع مطالعه: Original Paper | موضوع مقاله: 62Fxx: Parametric inference
دریافت: ۱۳۹۵/۷/۵ | پذیرش: ۱۳۹۶/۶/۱۸ | انتشار: ۱۳۹۶/۶/۱۸

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